VivaKitSMS spam detection
Imbalanced classes done right: precision, recall and why 98% accuracy can mislead.
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Our honest results
| Spam precision | 0.993 |
| Spam recall | 0.906 |
| Accuracy (86.6% of messages are not spam) | 0.987 |
What's included
- Readable source codeSmall, commented modules you can explain line by line in your viva.
- Demo appA local web app to show your examiners - runs offline on your laptop.
- Trained model + reference resultsSo you can compare your own runs fairly.
- Learning guide with viva Q&ABackground, design choices explained, limitations and extension ideas.
- One-click setup & testsWindows setup script and tests that prove it works on your PC.
- Dataset download scriptFetches the public data from its original source, with the citation.
Dataset credit
Almeida, T. & Hidalgo, J. (2011). SMS Spam Collection [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5CC84. Also: Almeida, T. A., Gómez Hidalgo, J. M., & Yamakami, A. (2011). Contributions to the Study of SMS Spam Filtering: New Collection and Results. ACM DocEng. - Creative Commons Attribution 4.0 International
Academic integrity
We sell code and teaching, not finished projects. You must understand, extend and write up the work yourself, acknowledge the kit and cite the dataset. No ready-made reports.